code: def one_sample(sample):
      mp3_filename = sample[0]
      # Storing wav files next to the mp3 ones - just with a different suffix
      wav_filename = path.splitext(mp3_filename)[0] + ".wav"
      _maybe_convert_wav(mp3_filename, wav_filename)
      frames = int(
          subprocess.check_output(
              ["soxi", "-s", wav_filename], stderr=subprocess.STDOUT
          )
      )
      file_size = -1
      if os.path.exists(wav_filename):
          file_size = path.getsize(wav_filename)
          frames = int(
              subprocess.check_output(
                  ["soxi", "-s", wav_filename], stderr=subprocess.STDOUT
              )
          )
      label = validate_label(sample[1])
      rows = []
      counter = get_counter()
      if file_size == -1:
          # Excluding samples that failed upon conversion
          counter["failed"] += 1
      elif label is None:
          # Excluding samples that failed on label validation
          counter["invalid_label"] += 1
      elif int(frames / SAMPLE_RATE * 1000 / 10 / 2) < len(str(label)):
          # Excluding samples that are too short to fit the transcript
          counter["too_short"] += 1
      elif frames / SAMPLE_RATE > MAX_SECS:
          # Excluding very long samples to keep a reasonable batch-size
          counter["too_long"] += 1
      else:
          # This one is good - keep it for the target CSV
          rows.append((wav_filename, file_size, label))
          counter["imported_time"] += frames
      counter["all"] += 1
      counter["total_time"] += frames
      return (counter, rows)
  
file_path: DeepSpeech/bin/import_cv.py
imports: import csv
  import os
  import sys
  import subprocess
  import tarfile
  from glob import glob
  from multiprocessing import Pool
  import progressbar
  import sox
  from deepspeech_training.util.downloader import SIMPLE_BAR, maybe_download
  from deepspeech_training.util.importers import get_counter, get_imported_samples, print_import_report
  from deepspeech_training.util.importers import validate_label_eng
label_desc: Use image node_id 4 for calling a global function with example usage: one_sample(sample) and returns: counter,  rows